Industry clusters have long been associated with generation of economic advantage. In recent times, the economic advantages of clusters are increasingly framed in terms of collective learning and innovation. Specifically, clusters can enable beneficial processes of collective learning among co-located firms and organisations, which in turn fosters enhanced rates of innovation, productivity, and new firm formation. This collective learning is rooted in processes of social interaction, observation, and imitation, and enabled by cluster-based socio-cultural and institutional structures.
However, the relationship between collective learning and industry clusters is not without contention. It is clear from the current literature, that the learning advantages of clusters do not arise automatically (localisation does not guarantee learning), are difficult to facilitate (cluster success stories are hard to replicate), and are challenging to maintain (once successful clusters can stagnate and decline). Particularly important unresolved questions include how the capacity for learning in clusters is actually developed in the first place, and what role (if any) strategic agency plays in this process.
In this thesis, I grapple with the aforementioned questions by proposing that before individuals, firms, and organisations can benefit from learning in a cluster, they must first learn how to cluster. ‘Learning how to cluster’ entails the reflexive collective learning processes through which cluster actors attempt to change how they cluster. It entails knowledgeable actors reflecting on how they act and interact in their shared practice, and on the basis of this reflection, implementing strategic collective action so as to enhance their clustering. In this way, ‘learning how to cluster’ contributes to the building of practices, social structures, and relations that make ‘learning in a cluster’ possible. ‘Learning how to cluster’ is thus a more basic or foundational form of collective learning, and to date has not been explicitly identified or studied in the cluster literature.
In order to deepen our understanding of ‘learning how to cluster’, I develop a practice-based approach to the study of industry clusters. A practice-based approach situates learning in social practices, and draws attention to ‘clustering’ as an ongoing social process. I utilise a particularly influential theory of practice – Anthony Giddens’s Structuration Theory – to suggest that clustering is the structurational process through which cluster actors recursively (re)produce the cluster through their practices. I then develop specific research questions to clarify the relations between the two distinct forms of collective learning – i.e. ‘learning how to cluster’ and ‘learning in a cluster’ – identified as significant in clustering. The practice-based approach is subsequently enacted via an interpretive, qualitative, case study analysis of two emerging biotechnology clusters. The nascent clusters are located in Brisbane and Melbourne, Australia, and are examined for a ten-year period of their development.
The Brisbane and Melbourne cases shed light on both the process and outcomes of clustering. First, in terms of process, the cases deliver new insights into the contribution of ‘learning how to cluster’ to clustering. The cases show how reflexive cluster actors jointly sought to alter how they acted and interacted in productive activity. Cluster actors reflected on their practice, identified shared problems and opportunities, and then collectively mobilised, organised, and instituted strategic action so as to augment their clustering. These findings indicate that the social structures enabling learning in clusters don’t just ‘emerge’; actors also deliberately and purposely create them. The cases thus challenge the prevalent notion of clusters as the predominantly unplanned outcome the cumulative actions and interactions of cluster actors over time. Rather, the cases suggest that reflexive learning and strategic action also play an important role in how clustering unfolds.
Second, in terms of outcomes, the Brisbane and Melbourne clusters displayed divergent clustering outcomes. Specifically, the Brisbane cluster was characterised by fragmentation and disconnection, and sharp conflicts across its distinct practices and communities. This was seen to hamper the ongoing capacity for the cluster to enable ongoing processes of ‘learning in a cluster’ and ‘learning how to cluster’. The Melbourne cluster, in contrast, displayed increasing integration and cohesion, and growing alignment of distinct practices and communities. This was seen to better support the ongoing possibilities for both ‘learning in a cluster’ and ‘learning how to cluster’. I link these divergent clustering outcomes to a new concept – boundary structures – and suggest that boundary structures are both the medium and the outcomes of collective learning in industry clusters. The notion of boundary structures suggests that a critical aspect of ‘learning how to cluster’ is developing and instituting the capacity to bridge social, symbolic, material, and geographic divides between practices and communities. In doing so, cluster actors are better able to align distinct yet related practices, enhancing the possibilities for ‘learning in clusters’.